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Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence
Trees are fundamental for Earth’s biodiversity as primary producers and ecosystem engineers and are responsible for many of nature’s contributions to people. Yet, many tree species at present are threatened with extinction by human activities. Accurate identification of threatened tree species is ne...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9100559/ https://www.ncbi.nlm.nih.gov/pubmed/35574125 http://dx.doi.org/10.3389/fpls.2022.839792 |
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author | Silva, Sandro Valerio Andermann, Tobias Zizka, Alexander Kozlowski, Gregor Silvestro, Daniele |
author_facet | Silva, Sandro Valerio Andermann, Tobias Zizka, Alexander Kozlowski, Gregor Silvestro, Daniele |
author_sort | Silva, Sandro Valerio |
collection | PubMed |
description | Trees are fundamental for Earth’s biodiversity as primary producers and ecosystem engineers and are responsible for many of nature’s contributions to people. Yet, many tree species at present are threatened with extinction by human activities. Accurate identification of threatened tree species is necessary to quantify the current biodiversity crisis and to prioritize conservation efforts. However, the most comprehensive dataset of tree species extinction risk—the Red List of the International Union for the Conservation of Nature (IUCN RL)—lacks assessments for a substantial number of known tree species. The RL is based on a time-consuming expert-based assessment process, which hampers the inclusion of less-known species and the continued updating of extinction risk assessments. In this study, we used a computational pipeline to approximate RL extinction risk assessments for more than 21,000 tree species (leading to an overall assessment of 89% of all known tree species) using a supervised learning approach trained based on available IUCN RL assessments. We harvested the occurrence data for tree species worldwide from online databases, which we used with other publicly available data to design features characterizing the species’ geographic range, biome and climatic affinities, and exposure to human footprint. We trained deep neural network models to predict their conservation status, based on these features. We estimated 43% of the assessed tree species to be threatened with extinction and found taxonomic and geographic heterogeneities in the distribution of threatened species. The results are consistent with the recent estimates by the Global Tree Assessment initiative, indicating that our approach provides robust and time-efficient approximations of species’ IUCN RL extinction risk assessments. |
format | Online Article Text |
id | pubmed-9100559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91005592022-05-14 Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence Silva, Sandro Valerio Andermann, Tobias Zizka, Alexander Kozlowski, Gregor Silvestro, Daniele Front Plant Sci Plant Science Trees are fundamental for Earth’s biodiversity as primary producers and ecosystem engineers and are responsible for many of nature’s contributions to people. Yet, many tree species at present are threatened with extinction by human activities. Accurate identification of threatened tree species is necessary to quantify the current biodiversity crisis and to prioritize conservation efforts. However, the most comprehensive dataset of tree species extinction risk—the Red List of the International Union for the Conservation of Nature (IUCN RL)—lacks assessments for a substantial number of known tree species. The RL is based on a time-consuming expert-based assessment process, which hampers the inclusion of less-known species and the continued updating of extinction risk assessments. In this study, we used a computational pipeline to approximate RL extinction risk assessments for more than 21,000 tree species (leading to an overall assessment of 89% of all known tree species) using a supervised learning approach trained based on available IUCN RL assessments. We harvested the occurrence data for tree species worldwide from online databases, which we used with other publicly available data to design features characterizing the species’ geographic range, biome and climatic affinities, and exposure to human footprint. We trained deep neural network models to predict their conservation status, based on these features. We estimated 43% of the assessed tree species to be threatened with extinction and found taxonomic and geographic heterogeneities in the distribution of threatened species. The results are consistent with the recent estimates by the Global Tree Assessment initiative, indicating that our approach provides robust and time-efficient approximations of species’ IUCN RL extinction risk assessments. Frontiers Media S.A. 2022-04-29 /pmc/articles/PMC9100559/ /pubmed/35574125 http://dx.doi.org/10.3389/fpls.2022.839792 Text en Copyright © 2022 Silva, Andermann, Zizka, Kozlowski and Silvestro. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Silva, Sandro Valerio Andermann, Tobias Zizka, Alexander Kozlowski, Gregor Silvestro, Daniele Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence |
title | Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence |
title_full | Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence |
title_fullStr | Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence |
title_full_unstemmed | Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence |
title_short | Global Estimation and Mapping of the Conservation Status of Tree Species Using Artificial Intelligence |
title_sort | global estimation and mapping of the conservation status of tree species using artificial intelligence |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9100559/ https://www.ncbi.nlm.nih.gov/pubmed/35574125 http://dx.doi.org/10.3389/fpls.2022.839792 |
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